Stratifying patients at the risk of heart failure hospitalization using existing device diagnostic thresholds

Abstract

Background: Heart failure hospitalizations (HFHs) cost the US health care system ∼$20 billion annually. Identifying patients at risk of HFH to enable timely intervention and prevent expensive hospitalization remains a challenge. Implantable cardioverter defibrillators (ICDs) and cardiac resynchronization devices with defibrillation capability (CRT-Ds) collect a host of diagnostic parameters that change with HF status and collectively have the potential to signal an increasing risk of HFH. These device-collected diagnostic parameters include activity, day and night heart rate, atrial tachycardia/atrial fibrillation (AT/AF) burden, mean rate during AT/AF, percent CRT pacing, number of shocks, and intrathoracic impedance. There are thresholds for these parameters that when crossed trigger a notification, referred to as device observation, which gets noted on the device report. We investigated if these existing device observations can stratify patients at varying risk of HFH.

Methods: We analyzed data from 775 patients (age: 69 ± 11 year, 68% male) with CRT-D devices followed for 13 ± 5 months with adjudicated HFHs. HFH rate was computed for increasing number of device observations. Data were analyzed by both excluding and including intrathoracic impedance. HFH risk was assessed at the time of a device interrogation session, and all the data between previous and current follow-up sessions were used to determine the HFH risk for the next 30 days.

Results: 2276 follow-up sessions in 775 patients were evaluated with 42 HFHs in 37 patients. Percentage of evaluations that were followed by an HFH within the next 30 days increased with increasing number of device observations. Patients with 3 or more device observations were at 42× HFH risk compared to patients with no device observation. Even after excluding intrathoracic impedance, the remaining device parameters effectively stratified patients at HFH risk.

Conclusion: Available device observations could provide an effective method to stratify patients at varying risk of heart failure hospitalization.

Keywords: Ambulatory monitoring; Heart failure; Heart failure hospitalization; Implantable device diagnostics; Intrathoracic impedance.

Fig. 1

Various device diagnostic parameters and corresponding default threshold values that trigger a device observation. The left column shows the various device parameters and their representative trend. The right column shows the corresponding default threshold values (see Methods for more details). Except for raw impedance and HRV, all other parameters have an observation that is triggered when the corresponding threshold is crossed. However, the availability of OptiVol observation varies with geography (refer to text for details).

Fig. 2

The schematic for diagnostic evaluation and risk assessment framework. The device observations occurring during the entire duration between two successive follow-ups (FUs) sessions were noted. Various follow-ups are indicated as FU1, FU2 etc. The look-back time window for evaluating device observations is labeled as ‘Risk Assessment’. The corresponding 30-day look-forward time window is labeled as ‘Risk Prediction’. Refer to text under the section Analysis Scheme for more details.

Fig. 3

Kaplan Meier curves for time to first HF hospitalization. Panel A shows HFH rate in the next 30-days following an evaluation for varying number of observations with OptiVol excluded from the device parameter set. Panel B shows an analogous plot with OptiVol included in the device parameter set.